RETRACTED ARTICLE: Studies of proteasome inhibition and antitumor activity by novel amino acid-polypyridine-copper complexes

Article abstract of DOI:10.1002/jhet.3732

Five innovative ternary copper (II) complexes [Cu (OH-PIP)(Phe)Cl](1), [Cu (OH-PIP)(Gly)(H₂O)]NO₃·2H₂O (2), [Cu (OH-PIP)(Ala)(Cl)]·H₂O (3), [Cu (OH-PIP)(Met)]PF₆·2H₂O (4), and [Cu (OH-PIP)(Gln)(H₂O)]Cl·3H₂O (5) have been synthesized and characterized by infrared spectroscopy, elemental analysis, and single crystal X-ray diffraction analysis. X-ray crystallography showed that all Cu atoms were five-coordinated in a square-pyramidal configuration. They are screened for in vitro cytotoxicity against a panel of human cancer cell lines CAL-51, MDA-MB-231, HeLa, MCF-7, SGC-7901, A549, K562, and SMMC-7721. The most promising results were achieved for complexes 1 to 5, with the IC₅₀ values in the range of 0.082μM to 0.69μM (against CAL-51 cell lines). The anticancer activities against CAL-51, MDA-MB-231, and MCF-7 were higher than that of Carboplatin. The mechanism studies showed that complexes 1 to 5 could inhibit proteasome activity, induce apoptosis, and inhibit cell proliferation, indicating their great potential as proteasome inhibitors for triple-negative breast cancer therapy.

Full text of DOI:10.1002/jhet.3732

CHI 2019 Paper
CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK
Seekers, Providers, Welcomers, and Storytellers:
Modeling Social Roles in Online Health Communities
Diyi Yang
Robert E. Kraut
Human-Computer Interaction
Institute
Carnegie Mellon University
robert.kraut@cmu.edu
Tenbroeck Smith
Behavioral Research
American Cancer Society
tenbroeck.smith@cancer.org
Language Technologies Institute
Carnegie Mellon University
diyiy@andrew.cmu.edu
Elijah Mayfield
Dan Jurafsky
Language Technologies Institute
Carnegie Mellon University
elijah@cmu.edu
Department of Linguistics
Stanford University
jurafsky@stanford.edu
ABSTRACT
supported cooperative work; • Computing methodologies
→ Cluster analysis; Discourse, dialogue and pragmatics.
Participants in online communities often enact different
roles when participating in their communities. For example,
some in cancer support communities specialize in provid-
ing disease-related information or socializing new members.
This work clusters the behavioral patterns of users of a can-
cer support community into specific functional roles. Based
on a series of quantitative and qualitative evaluations, this
research identified eleven roles that members occupy, such
as welcomer and story sharer. We investigated role dynamics,
including how roles change over members’ lifecycles, and
how roles predict long-term participation in the community.
We found that members frequently change roles over their
history, from ones that seek resources to ones offering help,
while the distribution of roles is stable over the community’s
history. Adopting certain roles early on predicts members’
continued participation in the community. Our methodology
will be useful for facilitating better use of members’ skills
and interests in support of community-building efforts.
KEYWORDS
Social Roles; Social Support; Online Health Communities;
Natural Language Processing; Machine Learning
ACM Reference Format:
Diyi Yang, Robert E. Kraut, Tenbroeck Smith, Elijah Mayfield, and Dan
Jurafsky. 2019. Seekers, Providers, Welcomers, and Storytellers:
Modeling Social Roles in Online Health Communities. In CHI Con-
ference on Human Factors in Computing Systems Proceedings (CHI
2019), May 4–9, 2019, Glasgow, Scotland UK. ACM, New York, NY,
USA, 14 pages. https://doi.org/10.1145/3290605.3300574
1
INTRODUCTION
A wide body of literature studying online health commu-
nities has developed and tested hypotheses on how these
communities differ from the internet at large, how users sup-
port each other, and how communities thrive over time. For
example, [52] studied how social support exchange in an
online cancer support group affects the length of people’s
participation, and [19] examined support exchange around
behavior changes in online weight loss communities. Using
descriptive statistical models, this research modeled charac-
teristics of user behavior to show that early actions result in
differential long-term membership trends. For instance, users
self-disclose more personal information in online health com-
munities than in parallel technical support communities, like
CCS CONCEPTS
• Human-centered computing → HCI theory, concepts
and models; Collaborative content creation; Computer
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CHI 2019, May 4–9, 2019, Glasgow, Scotland UK
Stack Overflow [5, 34]. Not all users display these behaviors,
though: for instance, many users join when facing crucial
healthcare events, like the start of chemotherapy, and are
seeking information for decision-making rather than hop-
ing to join a community [56]. Early actions and interactions
can be predictive of commitment. Newcomers looking for
informational support are significantly less likely to transi-
tion into long-term community membership, and those who
receive support are more likely to continue than those who
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CHI 2019 Paper
CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK
do not [52
,
58]. Yet 10% of support-seeking messages get
stay longer in the community, they are more likely to oc-
cupy the roles of emotional support provider and welcomer
and less likely to occupy roles such as story sharer and in-
formational support seeker. A closer look at members’ role
transitions suggests that they frequently change their roles
from seeking resources to roles that offer help to others. (3)
Both the tendency of certain roles’ occupants to drop out of
the community and the trajectory of roles in users’ lifecycle
in the community follow consistent patterns. These find-
ings suggest the value of the role framework as the basis for
intervention in online health communities, opening a new
opportunity for socio-technical systems to support users and
communities in their healthcare needs.
no replies, and many of the replies do not provide the sup-
port sought, as when long-time members provide emotional
support when the new user was seeking information [53].
Interaction in health support communities is in part the
products of the roles that members occupy [49]. For example,
some members might specialize in seeking support, provid-
ing disease-related information or socializing new members.
In contrast to roles in conventional organizations, where
roles are often assigned and come with defined responsibil-
ities, roles in most online communities are emergent. For
example, a user can assume an “expert” role in the commu-
nity without seeking permission from others. Researchers
have clustered lower-level behavior to identify roles in some
online communities like Wikipedia [54, 57]. However, few
studies have applied similar approaches to online health
communities [30].
2
ROLES IN ONLINE COMMUNITIES
Self-organized online communities are a novel area for the-
oretical exploration of emergent roles. In contrast to most
empirical studies of roles, which have looked at “formal” roles
The goal of the current paper is to study members’ par-
ticipation and coordination in online health communities,
and develop a taxonomy of the emergent roles that are ob-
served in these communities, linking individual behaviors
with community-level outcomes. Identifying emergent roles
can be beneficial for sustaining communities. Understanding
the roles that are important for a community and the roles
particular people are likely to occupy can help to optimize
user experiences. For example, information experts can be
matched to information seekers, giving the expert fulfilling
work to do while helping the seeker get timely responses;
welcomers can be matched to newcomers to ensure they re-
ceive timely support that will help them become integrated
into the community.
To this end, we propose a framework for defining social
roles in online communities together with a general model-
ing methodology. We use data from an online cancer support
community to identify behavioral features associated with
different facets of social roles. We then build an unsuper-
vised Gaussian mixture model from the data to discover 11
roles that members occupy. We validate these roles through
a series of quantitative robustness checks of the modeling
procedure, followed by confirmatory interviews with do-
main experts in the community. To demonstrate the utility
of the role model, we examine how roles predict the sta-
bility of activities on the site and participation by users as
they enter the community and evolve from being newcomers
to old-timers. (1) We find that occupying socially positive
roles, such as private communicator and story sharer, is as-
sociated with members staying in the community longer,
while members occupying roles such as informational sup-
port seeker are associated with lower long-term participation
in the community. (2) While the distribution of roles in the
community is relatively stable over time, members change
their roles frequently across their participation. As members
like leaders or moderators [17, 39], our work examines mem-
bers’ emergent roles in online health communities, which are
not structurally defined or constrained, but rather emerge
from common patterns of members’ behaviors. Theory on co-
ordination in groups and organizations has emphasized role
differentiation along with the division of labor associated
with roles as major mechanisms through which members
coordinate complex activities [10, 32, 33].
In the Structural perspective [22], the traditional model
for describing offline organizations, roles are generally for-
mally assigned, often in terms of a formal job title and pre-
scribed activities needed to fulfill the role well. These roles
are mainly based on formal and informal social expectations
and norms along with positive and negative sanctions to
enforce the norms. In online environments, the structural
model sometimes applies, including moderator roles in many
online discussion sites or administrator roles in Wikipedia.
In these cases, members have formal assignment to those
roles and clear expectations of responsibilities [1].
However, in the vast majority of online communities, roles
are emergent, self-selected and are often not formally rec-
ognized [2, 57]. As a result, although these emergent roles
constitute consistent patterns of behavior, neither the role
occupant nor other community members may have a clear
understanding of who is occupying which role or how role
occupants should behave. This pattern more closely matches
the interactionalist view of roles, which has built on several
decades of sociological theory research [12, 25, 50]. While
explicit roles have been studied in depth in online communi-
ties [18], the consequences of the more fluid, sociologically-
informed definition of emergent roles has received relatively
little attention in studies of behavior in online communi-
ties. The little research that does exist has largely focused
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CHI 2019 Paper
CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK
on production roles in collaborative projects like Wikipedia
[3, 54, 57, 60].
To begin to fill this gap, here we define social role as a
set of interaction patterns regulated by explicit or implicit
expectations and adopted by people in a social context to
achieve specific social goals. Our definition hangs on four
core facets of roles:
or they may come with no expectations at all. Because
these understandings may be implicit or known only
to long-time members, they can create barriers to com-
munity participation; for instance, on Stack Overflow,
fear of hostile feedback for improperly meeting expec-
tations of information seekers can prevent new users
from asking questions or joining the community in
the first place [24].
• Goal: Roles are associated with specific social goals.
Goals may serve the individual interests of the role
occupant, role partners or the groups in which the
roles are embedded [39]. For example, specific roles
may be adopted to facilitate collective effort toward
the completion of a task, such as a devil’s advocate
role in a course project team[47]. Roles can also be ori-
ented toward the long-term functioning of the group
as a whole, such as “Vandal Fighter” in Wikipedia [54].
Finally, people may take on some roles to satisfy their
individual needs or desires, such as newcomers acting
as information seekers to understand what the group
has to offer or senior members experiencing pleasure
in mentorship.
• Context: Roles can be very broadly applicable or lim-
ited to specific contexts. These contexts set boundaries
for role holders, i.e. delimiting the perimeter or setting
the scope of roles. For example, information provider
is a common role in many groups, including social
Q&A websites, health discussion forums, and problem-
solving groups; In contrast the committer role [51
]
is limited to open-source development communities.
Within a community, roles may be based on the pri-
vacy of the context, with people taking on a set of roles
in public while enacting others in private discussions.
Note that roles are performed by people [12]. Sometimes
people’s non-behavioral attributes such as their demograph-
ics like gender or race may be related to the roles they occupy.
Except in specialized cases, these characteristics may not be
an intrinsic part of roles, but they are often entwined with
expectations. For example, although Wikipedia bills itself
as is the encyclopedia that anyone can edit, men are much
heavier contributors than women [27].
The current research investigates members’ emergent,
behavioral roles when participating in online health commu-
nities independently of the demographics of the people who
occupy them. For example, any member can assume the role
of emotional support provider, no matter their gender, age or
cancer type. Our goal is to design a model that can ultimately
be deployed in online interventions, in environments where
both technical constraints and user privacy dictate that de-
mographics should not be a factor in the technical system.
Thus, we do not model personal attributes of members in our
research. Future studies in constrained, privacy-aware con-
texts may extend this work to directly cross the behavioral
roles identified with some of members’ personal attributes
(e.g., informational support provider × cancer type).
• Interaction: Roles are based on role holders’ charac-
teristic interactions, which can happen when role hold-
ers engage with other persons or objects, within or
outside the context where the role is enacted. These in-
teractions make up the core content of online commu-
nities. In discussion-oriented communities, these are
the threads-starting messages and comments through
which discussion takes place. But these interactions
also take place when role holders interact with the
user interface of the community’s website, or when
they speak with their spouse or friends outside. Such
interactions are observed by role holders, repeated
over time [50], and whether or not each interaction is
expected or approved by a role holder, each interaction
shapes the roles they may enact in the future.
• Expectation: Roles also involve expectations about
typical interaction patterns of persons [25, 29]. Adher-
ence to or departure from these understandings can
result in positive or negative sanctions from others
[15, 37]. Expectations are bidirectional: both the role
holders and the others with whom they interact often
have expectations about how the role holders should
behave and what they should believe. In conventional
organizations offline where roles are assigned, they are
generally associated with strong expectations; man-
agers in corporations speak differently when speaking
to their employees than they do when speaking to
bosses, for instance [16]. In many online communities,
though, roles are emergent. In these cases, there may
exist informal or implicit “negotiated understandings”
about how role occupants should conduct themselves
3
RESEARCH SITE
Our research was conducted on the American Cancer Soci-
ety’s Cancer Survivor Network1 (CSN), which is the largest
online support community for people suffering from cancer
and their caregivers. The CSN discussions boards are public
places where registered members can participate by start-
ing new threads or commenting on other members’ existing
threads. Registered members of CSN can also communicate
1https://csn.cancer.org/
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directly with each other using a function called “CSN Email”.
Conversations between two people are recorded in a for-
mat like email or private chat messages and are only visible
to individuals addressed in the message headers. We were
provided access to all public posts and comments, private
chats as well as the profile information for users registered
between Dec 2003 and Mar 2018. During this period, there
were a total of 66,246 registered users who exchanged 139,807
private messages, 1,080,260 comments and 141,122 threads.
This work was approved by Carnegie Mellon University’s
Institutional Review Board (IRB).
This model has three key parameters that need to be set
by researchers: the behavior features
X
, the length of user
. In the
representation
l
, and the number of implicit roles
K
following, we describe the procedures used to set each pa-
rameter and the steps taken to design robust models.
Operationalizing Behavioral Features
To extract the emergent roles that members take on when par-
ticipating on CSN, we identified a set of behavioral features
that operationalize the four components in our definition of
role definition described above: goal, interaction, expectation
and context.
Recently, deep learning based techniques have been pro-
posed to learn user embeddings based on their interactions in
an end-to-end manner [26, 28, 44]. Although that approach
4
METHOD FOR ROLE IDENTIFICATION
Our method of identifying emergent social roles in online
communities is a repeated cycle of role postulation, definition,
automated processing and evaluation. When participating in
the community, a user takes on one or more implicit roles
for their activities. In their future interactions, they may take
on the same roles or shift roles. To model this, we define a
Gaussian mixture model [36], a statistical model that clus-
ters heterogeneous user-session representations into a set
of coherent, discovered user roles. Unlike traditional unsu-
requires less domain knowledge and manual feature con-
struction, it suffers from lack of interpretability especially
about the nature of discovered roles and the people who oc-
cupy them. In terms of techniques for identifying social roles
online, most research employed clustering analysis or princi-
pal component analysis to cluster each user into one or more
clusters [54, 57]. To make the derived roles interpretable,
we followed this common practice to construct explainable
patterns to capture members’ role-relevant behaviors.
Goal (9 features). Many people with chronic illnesses, in-
cluding cancer patients and survivors, participate in online
health support groups. Ridings and Gefen found that 76%
of people who joined online health groups were looking for
two types of social support [45] - informational support and
emotional support. Informational support contains informa-
tion, advice, or knowledge, and emotional support refers
to the provision of empathy, sympathy or encouragement.
pervised learning such as -means clustering, in which an
k
object can only be a member of a single cluster, a mixture
model allows users to occupy multiple roles during a session
(e.g., a welcomer and information provider).
The model assumes that user activities can be described
by a set of observable behaviors
X
, and there exist
k
compo-
nents per role {cik=1
}
. Each component ci has an associated
. A user’s
vector µi of average values for each feature in
X
activity is generated from a mixture of these components and
a covariance matrix Σi , representing the likelihood of each
role co-occurring with each other role. Formally, Gaussian
Mixture models are a linear combination of Gaussians, with
a probability density function as follows:
Building on prior studies on social support [13, 52], we op-
erationalized a set of goal-oriented actions that members
exchange in the context of support groups. This resulted
in 4 features of linguistic behaviors: seeking informational
K
Õ
Õ
support
, providing informational support, seeking emotional
p (x) =
πk · N (x|µk , Σk ) , where
πk = 1
support, and providing emotional support.
k=1
k
We observed from our data that people tend to employ
very specific language strategies when providing emotional
support to others. Some choose to show empathy, saying
that they understand what the recipient is going through and
identify with their emotional reactions and feelings. Some
express encouragement and hope that others’ situations will
improve. Others show appreciation for others’ accomplish-
ments to increase others’ senses of worth, value and compe-
tence. To capture these nuanced intentions, we differentiated
three finer-grained sub-categories of providing emotional
Here, {πiK=1
}
are called mixing coefficients, and each user will
be assigned a coefficient πi for each role ci . The coefficient
represents the proportion of a user that was associated with a
particular role; each user unit is modeled as a mixture of roles,
which enables us to capture participants’ versatility and dy-
namics in the online community. When building this model,
we need to learn mixing parameters {π , π , . . . , πK }, means
1
2
{µ , µ , . . . , µK } and covariances {Σ , Σ , . . . , ΣK } from data
1
2
1
2
N
{xi }i=1. Here, each xi is a heterogeneous vector of features
extracted from each user, while represents the total num-
N
support: providing empathy, providing encouragement, and
ber of user units in our corpus. Given a large corpus of data,
we can estimate the covariance matrices by positing that
each component has its own general covariance matrix.
providing appreciation. In addition to exchanging social sup-
port, members also share their experiences and stories to
help others understand who they are and to provide social
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Goal-oriented conversational acts ICC Correlation
anxiety, sadness, or anger related words, were used as in-
dicators of members’ emotional orientation. To figure out
whether members talked about their personal relationships,
we counted their usage of words related to family and friends
via corresponding dictionaries in LIWC. Similarly, members’
religious orientations and emphasis on themselves vs others
(interpersonal pronouns) were calculated via related dictio-
naries. In total, 16 features were extracted via using corre-
sponding LIWC categories. Topic modeling [14] was con-
ducted to derive topics that members discuss with others on
CSN, resulting in 25 topics including prayer, surgery, radia-
tion, clinical trials, and chemotherapy side effects. One fea-
ture is included for each topic. We also incorporated domain
knowledge from Freebase to capture 4 features counting
members’ use of words related to disease, medicine, ingre-
dients, and symptoms in their messages when providing
information to others. To identify potentially knowledgeable
CSN members, we extracted two features: the number of
external links and the number of words in messages.
seeking informational support
providing informational support
seeking emotional support
providing emotional support
providing empathy
0.91
0.92
0.83
0.92
0.74
0.68
0.73
0.90
0.90
0.73
0.79
0.64
0.75
0.72
0.64
0.67
0.72
0.71
providing encouragement
providing appreciation
self-disclosing positively
self-disclosing negatively
Table 1: The intra-class correlation and correlations between
human decisions and predictions for 9 conversational acts
comparison information [21]. Thus, we also considered the
language people use to self-disclose via two additional fea-
tures: self-disclosing positively and self-disclosing negatively.
Automatic text analysis techniques can accurately mea-
sure the amount members’ messages contain each of these
nine features. Four trained nursing students rated a sam-
ple of 1,000 messages threads and their first responses for
degree they represented these nine goal-oriented conversa-
We then looked at interaction patterns that emerge from
users’ social networks in the online community. Previous
studies demonstrated methods for revealing network struc-
ture and people’s relationships with other users [23, 54, 55].
For this purpose, we constructed a user-reply network and
extracted features through network analysis, where the ver-
tices represent members who have participated in at least one
messages, and edges represent replies. For example, an edge
tional acts. Using previously developed procedures [13, 52],
we built machine learning models to predict the students’ as-
sessments of the nine conversational acts in messages. These
machine learning models map a set of linguistic features,
from user
u
to user
v
means that
u
replied to ’s messages.
v
From this graph, we extracted six network-based features: (1)
To capture the centrality of members’ role in the social struc-
ture, we calculated their (1) in-degree and (2) out-degrees.
To capture tenure effects we measured (3) members’ ratio of
talking to newcomers and (4) being talked to by old-timers.
(5) To measure whether users talk mainly to several specific
users or broader audiences, we calculated the entropy of the
user-user interaction distribution. Here, a higher entropy
means users talking to broader audiences. Finally, to mea-
sure a user’s breadth of interests, we measured the number
of sub-forums a person has posted in, where each sub-forum
represents one cancer type.
as described in [52, 59], to a set of continuous output val-
ues, indicating how much informational support, emotional
support, positive self-disclosure, and negative self-disclosure
a thread-starting message conveys as well as how much
informational support, emotional support, empathy, encour-
agement, appreciation, positive self-disclosure, and negative
self-disclosure responses provided. Human annotation agree-
ment on a training dataset was high (mean ICC=.84), and the
machine learning models achieved reasonable correlation
with the average of the human judgments (mean Pearson
r=.71; see Table 1). We then applied these models to estimate
the nine conversational acts in all messages in our corpus.
Separate from these automatic annotations, we also ex-
tracted 2 features measuring raw activity count for users - the
number of threads initialized, and the number of comments.
Expectation (2 features). Emergent roles may be associated
with informal implicit “negotiated understandings” among
individuals about what persons should do if they seem to
occupy such roles. Members on CSN might indicate such
positive or negative evaluations of others via their language
choices such as complaining to administrators or telling oth-
ers what to do. To this end, we extracted two features: (1)
the number of messages members exchanged with moder-
ators and (2) their usage of modal words such as “should”,
Interaction (53 features). The actions members take toward
achieving their goals are essential for understanding the
roles they occupy. In this part we use two methodologies to
extract interaction features: linguistic and network-based.
We developed linguistic indicators of members’ topical
interests by comparing each person’s word usage with se-
mantic categories provided by the psycho-linguistic lexicon
LIWC [42]. The presences of affective expressions such as
“
could”, and “must”. Here, modality in members’ messages
may convey their suggestions, request or advice to others.
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Context (17 features). The context of communication matters.
For the purposes of this study, we focused on public vs private
conversations as the context. Members may talk to others in
private chats to protect their personal information or interact
with them on the public discussion board. To capture mem-
bers’ potential concerns of privacy, we differentiated all 9
Goal features and their 6 network-based Interaction features
into separate values for communication in private chats and
in the public forum. For example, seek informational support
will have two features: seek informational support in private
chats2 and seek informational support in the forum. Similarly,
being talked to by oldtimers becomes being talked to by old-
timers in private chats and being talked to by oldtimers in the
forum. Note that this domain differentiation is a common
in this community are more variable over time. This variabil-
ity led us to examine transitions between roles, described in
more detail below.
Role theory also states that role are based on multiple
interactions [50], suggesting that detection of roles based
on only one observed action is impossible. To address this,
we conducted a sensitivity analysis removing sessions that
had fewer than
t
actions (t ∈ {
1
,
2
,
3 ). We did not observe
}
any significant changes in the derived roles. For all analyses
below, we follow the 24-hour inactivity threshold to define
sessions and include all sessions, without removing ones with
few actions. In total, this resulted in 517,272 user-sessions
from 66,246 users.
Determining the Number of Roles
practice in text representation for statistical modeling [35
]
as well as in social computing research [ ]. Finally, we cal-
8, 9
Qantitatiie Setinn of Upper and Lower Bounds. The number
culated 1 feature that measures the ratio of members’ private
communication to all their private and public activities to
capture their preferences for different contexts.
of roles
K
in this model is a free parameter and is the element
most susceptible to over-tuning [46]. We used the Bayesian
Information Criterion (BIC) to select the number of compo-
nents in the Gaussian mixture model (GMM). We trained
Gaussian mixture models on the user-session corpus and
Determining the Granularity of User Activity
experimented with
K
ranging from 2 to 20 to determine the
Determining the unit of analysis for appropriately repre-
senting members’ activity is key decision in modeling social
roles. Treating users as an aggregation of all their historical
actions on CSN prevents one from examining the evolution
of roles or transitions between them. On the other hand,
employing very small time intervals, such as a single user
action, might miss important larger constructs like a cluster
of actions needed to achieve a goal. In this analysis we use
aggregated data from each user session, which is defined
as a time interval in which the time gap between any two
adjacent actions is less than a threshold (24 hours). Within
sessions, users’ behaviors were regarded as consistent. We
operationalized the 83 features described above to capture
members’ behaviors within each session.
To test the robustness of the role models, we explored
the degree to which they varied across different temporal
units–all activity within each calendar day, week, or month.
We found that frequently-occurring roles were consistent
across different settings. The roles that emerged using a cal-
endar day as the unit of analysis were very similar model to
those emerging from session-level modeling, likely due to
the similar time-scale. As the temporal unit increased from a
day to a week to a month, the derived roles became harder to
interpret. This suggests that unlike assigned roles in offline
organizations (e.g., professor in a university), emergent roles
optimal number of components/roles. We found that models
with K ∈ [10, 15] seemed to be a good fit.
Qalitatiie Validation of Final Setinn. Validating these behav-
ioral role components inferred from unsupervised methods
is challenging. Existing work on similar tasks such as LDA
topic modeling has tried to validate the derived components
by asking people to provide summary labels for each com-
ponent [14
,
41] or by measuring the purity of the clusters or
38]. However, interpreting topics or compo-
components [20
,
nents by researchers themselves might introduce biases, and
defining the purity of components that consist of member
behaviors rather than simpler features, like bag-of-words
representations of topics, is hard to operationalize.
To overcome these problems, we followed a qualitative
protocol to finalize the number for user roles and their names.
We ran the Gaussian mixture model with our behavior fea-
tures and user-session length for different values of . We
K
then discussed the extracted components with 6 domain
experts (5 moderators from CSN and a senior researcher fa-
miliar with the site). We used their input to help interpret
the latent components. We showed the domain experts the
top ranked features associated with each role as well as three
users who were most representative of each role (i.e., the
three users from each role component whose behaviors were
closest to the centroid representation of that component).
The details about our semi-structured interview with domain
experts is here3. Based on their input, we set K=11.
2For privacy concerns, annotators are not allowed to view and annotate
private messages. In these cases, we applied the trained regression models
from public forum posts to predict 9 conversational acts in private mes-
sages. Accuracy may be lower in these contexts, as this prediction requires
transferring the model to a slightly different domain.
3http://www.cs.cmu.edu/~diyiy/docs/csn_role_interview_instruction.pdf
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Role Name
%
Typical Behaviors
Emotional
Provide emo support in the forum, provide appreciation in the forum, provide encouragement in the forum,
# subforums a user participated, provide empathy in the forum, provide info support in the forum
out-degree in forum, # replies in the forum, the ratio of talking to newcomers in the forum
provide encouragement and provide empathy in the forum, the entropy of user-user interaction in the forum
Provide info support in the forum, provide empathy in the forum, provide encouragement in the forum,
mention symptom related words, mention drug related words, mention anxiety related words
# threads in the forum, self-disclose positively in the forum, seek emo support in the forum,
self-disclose negatively in the forum, seek info support in the forum, use interpersonal pronouns
# threads in the forum, seek info support in the forum, self-disclose negatively in the forum,
seek emo support in the forum, mention disease related words, mention symptom related words
Provide emo support in private chats, provide appreciation and provide empathy in private chats,
provide info support and provide encouragement in private chats, self-disclose positively in private chats
Preference for using private chats, provide encouragement and provide info support in private chats,
provide emo support in private chats, provide empathy in private chats, seek info support in private chats
# messages in private chats, provide appreciation in private chats, provide emo support in private chats,
provide encouragement in private chats, # replies in the forum, self-disclose positively in the forum
# threads in the forum, seek info support in the forum, self-disclose positively in the forum,
self-disclose negatively in the forum, seek emo support in the forum, mention diagnostic test related words
# urls/links per message, mention ingredient related words, provide info support in the forum,
mention drug related words, mention anxiety related words, mention death related words
33.3
support provider
Welcomer
15.9
13.3
10.2
8.9
Informational
support provider
Story sharer
Informational
support seeker
Private
support provider
Private
communicator
5.3
5.3
All-round expert
2.5
Newcomer
member
Knowledge
promoter
2.4
2.2
The entropy of user-user interaction in private chats, out-degree in private chats, in-degree in private chats,
# messages in private chats, the ratio of being talked to by oldtimers, # private conversation initialized
Private networker
0.8
Table 2: Derived roles and their representative behaviors ranked by their frequency (%) in descending order.
5
DISCOVERED FUNCTIONING ROLES
(4) Story Sharer: people who disclose personal informa-
tion and emotions in order to receive support. They
share their own experiences and stories in an intro-
spective and verbose manner, which might help similar
users and/or inform potential support providers about
their situations.
(5) Informational Support Seeker: people who ask ques-
tions and seek information from others in public fo-
rums. Members with this role initialize more threads,
and seek around 1.7 standard deviations more infor-
mational and emotional support than average. They
also talk more frequently about metastasis and other
aspects of their disease.
(6) Private Support Provider: people who use private
chats to provide social support to others. People in this
role provide emotional support, encouragement, appre-
ciation and information to others in private chats, as
well as self-disclose in a positive manner to encourage
others.
(7) Private Communicator: people who are protective
of their personal details and only choose to partici-
pate in private chats. They seek and provide different
types of support such as informational support, empa-
thy and encouragement, and have strong tendency to
After final parameter tuning and validation from discussions
with domain experts, we have evidence that the model is ef-
fective in identifying latent roles that members occupy. Once
these parameters were set, we worked with the 6 domain
experts to co-develop short names and interpretable descrip-
tions of each component in the model, describing the roles
that emerged. These roles, their frequency in the corpus, and
highest-probability features are described in Table 2.
(1) Emotional Support Provider: people who respond
to others with empathy, encouragement and emotional
support. These active forum members participate in
a number of sub-forums, in contrast to most users
on CSN who only participate in one sub-forum most
relevant to their cancer type.
(2) Welcomer: people who respond to newcomers after
they first post on CSN. These higher-tenured mem-
bers interact with newcomers frequently and provide
supportive empathy and encouragement.
(3) Informational Support Provider: people who of-
fer information and advice to others in the discus-
sion board. This group of members discusses cancer-
specific issues by mentioning symptoms and ingredient-
related words, and provides information to others.
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Role
HR
0.984
0.883***
Std.Err
communicate privately (3.7 standard deviations more
frequently than the average level).
Emotional support provider
Welcomer
0.027
0.028
0.034
0.034
0.023
0.033
0.022
0.028
0.025
0.028
0.035
(8) All-round Expert: people who engage in a large set
of support exchange behaviors in both public discus-
sion board and private chats. This group of members
active engages and performs various kinds of actions
such as providing appreciation in private chats, re-
plying to others and self-disclosing positively in the
forums.
(9) Newcomer Member: people who ask questions and
seek support shortly after joining CSN. Most members
in this group stay at CSN for less than one month. They
use the discussion board to ask for both informational
and emotional support, and emphasize the uncertainty
associated with cancer diagnosis results (0.8 standard
deviation more than average).
Informational support provider 1.060
Story sharer
0.872***
Informational support seeker
Private support provider
Private communicator
All-round expert
1.324***
0.842***
1.031
0.869***
1.054***
1.091***
0.916*
Newcomer member
Knowledge promoter
Private networker
Table 3: Survival Analysis predicting how long members
continue to participate in the community. p<0.001: ***;
p<0.01**; p<0.05*. Number of users = 66,246. Number of user-
session records = 522,429
(10) Knowledge Promoter: users who post links and in-
formation from outside CSN. Those users present them-
selves as knowledgeable about what they are talking
about and recommend external research pointers to
members in need of help. Compared to regular mem-
bers, knowledge promoters share two standard devia-
tions more links in their replies to others.
(11) Private Networker: people who seem to be network
hubs in private chats. Although they participate in
the discussion forum and exchange social support in
private chats from time to time, they talk to a larger
set of members in private chats and exchange more
messages compared to other members.
“there are not a lot of them, but they stick in your
memories since they are telling others what to do.“
The defender role likely does exist on CSN, but our model
did not capture it, either because the behaviors that charac-
terize the defender role occur infrequently or the features
we used to characterize user-sessions did not reflect these
behaviors.
6
INFLUENCE OF ROLES ON COMMITMENT
Members’ patterns of activities and roles can influence their
contribution and commitment to the community. Although
previous research has investigated members’ commitment to
After discussion with domain experts, we obtained agree-
ment on the name and characteristics of 10 of the 11 derived
roles. However, we failed to achieve consensus for all-round
expert4. Despite this, domain experts agreed that the set of
behavioral roles we identified were comprehensive:
both offline and online organizations [6, 31, 58], no computa-
tional research has examined how members’ assumption of
emergent roles relates to commitment in online health com-
munities. This section examines how emergent roles help
predict continued participation of members on CSN. Doing
so will allow us to better understand members’ engagement,
as well as demonstrate the utility of our derived roles.
We use survival analysis to investigate how members’ oc-
cupation of social roles correlates with the length of their
participation on CSN. Survival analysis is a type of regres-
sion analysis for estimating influences on the time to an
event of interest, especially for censored data. In our context,
the event is defined as members dropping out of CSN. We
used Stata survival command with a Weibull distribution of
survival times in order to perform this analysis [48], with
the unit of analysis being the user-session. Control variables
included the member’s gender, whether the member had
cancer, and his/her tenure (i.e., how many months they have
stayed at CSN). Since the continuous explanatory variables
were standardized, the Hazard Ratio (HR) is the predicted
change in the probability of dropout from CSN for a standard
“It seems very comprehensive and there are so many
different examples, so I feel like it is covered very
well with your different roles and labels.”
Domain experts did point out roles that our model did
not capture. For instance, they identified “Guardian” or “De-
fender” role - people who fight with spammers or violate
norms on CSN, trying to regulate others’ behaviors. One of
the domain experts described the defender role this way:
“The one that I think did not emerge is the police-
man, these people complain to moderators when
some people are doing things wrong or tell other
people that they are violating norms. They shouldn’t
be diagnosing the way that they are diagnosing or
other sorts of problems.".
4We urge readers to interpret our follow up analyses about all-round expert
with caution.
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deviation increase in the predictor. A hazard ratio greater
than one means the role is associated with a higher than
average likelihood of dropping out, while a hazard ration
less than one means a lower than average likelihood of drop-
ping out. Because of the correlations between different roles,
and correlations among roles and tenure, we built separate
survival models for each role, resulting in 11 models.
Results of the survival analyses are shown in Table 3. The
analyses show that members occupying certain roles - knowl-
edge promoter, informational support seeker and newcomer
member - are less likely to continue in CSN (i.e., lower sur-
vival rates). Specifically, members who were one standard
deviation more likely to occupy informational support seeker
roles were 32.4% more likely to leave the community after
that session. Similarly, members who were one standard de-
viation more likely to be newcomer-seekers were 5.4% more
likely to drop out from the community, while members who
share external knowledge with others on CSN (knowledge
promoters) were 9.1% less likely to continue their partici-
pation. These results suggest that roles related specifically
to information-sharing are associated with higher rates of
drop-out, possibly because researching disease or treatment
relevant information is a distinct, time-consuming use of
online resources, separate from community-building goals.
These members may see CSN as a more transactional re-
source, either giving or receiving information, and represent
a less committed user.
Figure 1: The percentage of different role occupations
substantially over time. This demonstrates that although
new members join and old members leave, organization-level
compositions in terms of emergent role behaviors remain
stable. A closer look at the year-by-year role composition
revealed that informational support provider increased to
25.5% in 2017 from 11%-13% in earlier years (2004 2015). We
also observed a weak increase for newcomer seekers, likely
due to large increase in active forum users after 2015. In
contrast, the percentage of welcomers in the community
decreased to 4% in recent years, perhaps suggesting that old-
timers, who dominate the welcomer role, are becoming less
welcoming to newcomers or less polite over time.
In contrast, occupying roles such as private networker,
private support provider, newcomer welcomer, and story
sharer are associated with members staying at CSN longer.
This may be because being support-providers to others en-
courages members to interact with other members time after
time, developing stronger relationships. People who respond
to newly registered members with support were 12% more
likely to stay on CSN; members who were willing to self-
disclose their experiences to seek support or benefit others
had a 13% higher survival rate.
Individual Level Dynamics
Changes in Role Occupation Over the User Lifecyle. When
members first join CSN, they may have high uncertainty
about the type of people who are members and the group’s
7
STABILITY AND DYNAMICS OF ROLES
norms [7]. Over time those who stay may accumulate expe-
As members go through their life cycles, they might choose
to drop out or stay on CSN. The roles of those who stay might
change over time. For example, as previously described by
the Reader-Leader framework [43], people may change from
being peripheral to core members of the community. In this
section, we examine whether members’ emergent roles vary
over their tenure at CSN, and we test the stability of users’
emergent roles at both individual- and community- levels.
rience in terms of both domain knowledge related to their
diseases and the group and its norms. This knowledge may
increase people’s ability to give back to the community. To
investigate whether higher tenured members occupy a dif-
ferent set of roles than newcomers, we compared role associ-
ated with members’ tenure in CSN, as described in Figure 2.
Specifically, we looked at members’ role occupation in their
first month - (0, 1], from their second month to six months -
(1, 6], from six months to a year - (6, 12], and after one year -
(12, +]. Among 66,246 members, 93% of users participated in
CSN in their first month after registering. Figure 2 shows
that emotional support providers, welcomers, informational
support providers, story sharers and informational support
Community Level Stability
We first investigated the mixture of roles in the forum overall
over a thirteen years period (see Figure 1). The frequency of
the majority of the behavioral roles on CSN did not change
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Figure 2: The percentage of role occupation for users by their CSN tenure among user who participated in CSN for at least a
year. (0, 1] refers to members’ first month in CSN, (1, 6] refers to their second to sixth month, (6, 12] refers to their six months
to one year and (12, +) refers to after one year.
seekers were the most common roles. During members’ first
month on CSN, roughly 20% of them occupied the role of
information support seeker, and 15% choose to share their ex-
periences and stories to start their conversations. As tenure
increases, members were more likely to occupy the role of
emotional support provider, private support provider and
private networker. In contrast, members are less likely to
occupy the story sharer and information support seeker roles
the longer they stayed on CSN, while they were more likely
to be newcomer welcomers after their first month. Although
Figure 2 includes only users who have been at CSN for a
year, similarity results obtain for users with who have been
at CSN for less than 12 months or less than 6 months.
We calculated the presence of each role transition pattern by
looking at members’ roles in any adjacent sessions. Here, a
user is said to occupy a role in a session if that role had the
largest weight across the 11 roles. We also model a user’s
likelihood of dropping out (i.e., discontinuing participation
in CSN) after occupying a role. This produces 132 total pos-
sible transitions (11 x 12, where the one added transition
probability leads to dropout).
We described the most common transitions overall in Ta-
ble 4. Since 70% members dropped out of CSN after 30 days,
we calculated this transition pattern only for members who
stay on CSN longer than that. We found that private com-
municators are the most stable role, at 41.3% carryover from
session to session; users who take on this role are more likely
to maintain it in their next session compared to any other
role. Not only do users who provide emotional support in
one session tend to continue in that role in the next session,
but it is the most common role for users to transition into
from other roles - 33.5% of welcomers, 36.2% of informational
support providers, 32.6% of information support seekers and
31.2% of story sharers. The conditional probability of transit-
ing from informational support seekers to emotional support
providers is 0.326, confirming the typical transitions from
outside observers into core members of the community [43].
This also reflects the rule of reciprocity that members who
seek resources eventually give back to their communities.
This showed that members transit from roles that seek for
resources to roles that offer help to others. The emotional
support provider role derives its stability partially from being
a role associated with longer-term users, rather than new-
comers. We show this by next deriving transition matrices
conditioned on session. Figure 3 shows the results for two par-
ticular session transitions: from session 1 to session 2 (left
side), indicating the first step of users from newcomers to
group membership; and from session 10 to session 11 (right
Role transition pattern
private communicator → private communicator 0.413
info support provider → emo support provider 0.362
Prob
emo support provider → emo support provider 0.336
welcomer → emo support provider
newcomer member → emo support provider
info support seeker → emo support provider
private networker → private communicator
story sharer → emo support provider
story sharer → welcomer
0.335
0.330
0.326
0.315
0.312
0.207
Table 4: The top 9 most frequent role transition patterns.
Role Transition Processes. These results suggest that mem-
bers assume different roles in different stages of participation.
To further investigate role evolution, we examined the pro-
cess of members’ moving from one role to another across
sessions. Specifically, we model users’ role transitions as a
Markov process, i.e., if a user assumed a particular role dur-
ing session , what is the probability that he or she would
i
take on any specific one of the eleven roles in session i + 1?
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Figure 3: Conditional probability of role transitions from one session (row) to another (column) after the first (left) and tenth
(right) session.
side), as an example of the more stable matrix that emerges
as users become long-term members.
this multiple role transition by modeling the mapping from
ꢀ
ꢁ
2K roles to 2K roles and dropout, resulting in a 2K × 2K + 1
We found three distinct groups of newcomers. The first
group does not follow any of the public roles that engage in
broader discussion forum, but instead use the site primarily
as a vehicle for private conversations, such as from private
communicator to private communicator (25.4%). The second
group is primarily information seekers, who then transition
into providers (of both informational and emotional sup-
port) and welcomers in their follow-up sessions. The third
common group, story sharers, are notable for their very low
dropout - 64.2% of story sharers return for a second session
on CSN, compared to 35.5% of first-time users that assume
all other roles combined.
matrix compared to a K × (K + 1) matrix in Figure 3. For
example, how do people transit from informational support
seeker, newcomer seeker to emotional support provider, wel-
comer. However, such a complete approach might run into
challenges with data sparsity, so the right course of action
will likely be to investigate the tradeoffs in representation.
8
DISCUSSION
This research investigated the functional roles that members
occupy in an online cancer support community, and how
such role occupation influences their engagement within
their communities. We first introduced a generic framework
to define emergent roles in online communities with four
components - goal, interaction, expectation and context. We
operationalized a set of behavioral features to represent
each component and then employed unsupervised models
to extract the functioning roles that members occupy, which
discovered 11 interpretable roles in online cancer support
groups.
As tenure increases in the 10th session transition matrix,
members are likely to transition out of the role of infor-
mation support seeker and story sharer, and more likely to
transition into the role of emotional support providers and
welcomers. These roles are common and “sticky” - users have
high probabilities of maintaining that role from session to ses-
sion. Private support providers and private networkers were
present at high rates among longer-term users, and maintain
their roles over time. While support providers transition into
their roles over time, private networkers were more likely to
have taken on this role early in their tenure.
Note that for role transition analyses, we used a heuristic
rule and treated each user in a session as occupying a single
role - the role with the highest weight - to model the process
of role transition. Since users can occupy hybrid roles, it is
possible that co-occurring roles might affect our role transi-
tion results. For example, users transit from one set of roles
to another set of roles in their next sessions or dropout if
they did not have a next session. Future work could address
Among the few studies that investigated emergent roles in
online communities, most have paid attention to platforms
such as Wikipedia [2, 4, 57]. Previous research in online
health communities suggested that there are distinct subsets
of users with different “roles” [58], but had no formal meth-
ods of modeling what those subsets were. We extend this
line of work into another type of community - to the best of
our knowledge, the first work to use data-driven methods to
identify behavioral roles in online health communities. Some
of the prototypical behaviors associated with the roles we
derived correspond to roles in conceptual frameworks; for
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instance, our “informational support seeker” and “informa-
tional support provider” correspond to “information seeker”
and “information giver” [11]. The role of “emotional support
The derived roles can be incorporated as additional features
for connecting users to other users, content and tasks based
on their roles along with other information about them (e.g.,
their disease, expertise or, emotional support needs). In ad-
dition to the potentials in boosting the recommendation
performance, members’ functioning behavioral roles can
also be used as explanations to users about why such recom-
mendations are made. For example, instead of “You might be
interested in ...,” the recommendations can be explained like
provider” seems to reflect the role of “encourager” [39, 40],
which involves showing understanding and acceptance of
others’ ideas and suggestions.
In addition to helping define these roles, this generative
model to describe subsets of users can both identify a user’s
assumption of a role in real time, and model how an individ-
ual member is likely to transition across roles over time. Most
earlier research on role identification used limited metrics
in evaluating roles, and statistical models more well-suited
to analysis of static datasets, rather than real-time predic-
tion in a machine learning architecture. These models also
required metrics of success such as model fit or manual la-
beling, suffering from potential biases and lack of domain
knowledge. To overcome such issues, in addition to quan-
titative validation of model fit, we followed through with
in-depth interviews with 6 domain experts who have a deep
understanding of CSN. The results of these interviews sup-
port the validity and quality of our derived roles. We believe
that most existing empirical methods for identifying roles
“
This is an information expert who can help you with breast
cancer.” Online communities could also introduce some of
these derived roles as badges to encourage users to assume
these roles and reward those who do.
Limitations
This research has significant limitations. While it is an ini-
tial step towards understanding emergent roles in online
support groups, we do not have self-reported evaluations
from CSN members about their perceived role occupations.
Although we validate our derived roles with a set of domain
experts, future work surveying members who tend to oc-
cupy such roles will allow us to compare model predictions
with user-perceived role occupation. Second, while we make
correlative descriptions of members’ role occupation and
their engagement on CSN, our work is not causal. Thus oc-
cupying socially positive roles may motivate users to stay
longer, but alternatively, new users who were more likely to
maintain membership may be more likely to perform such
roles, reversing the causal link. While this research looks at
one online cancer support group, we cannot necessarily gen-
eralize findings to other online health communities without
further work. Finally, the opportunity to use role predic-
tions to alter user experiences and make recommendations
has important ethical considerations. We have developed
a model with the potential to predict users’ future behav-
iors in online communities, and adjust their user experience
based on those predictions. However, such models have the
potential to become a self-fulfilling prophecy, shepherding
users into a particular activity path without giving them the
full breadth of opportunity to explore other roles. As this
research evolves into interventions, a crucial element for
analysis will be interviews with members, observation of
changes in their behaviors compared to baseline conditions,
and an interdisciplinary analysis on the changed outcomes
for users - particularly vulnerable, healthcare-seeking users
- in these and similar communities.
in other domains [2, 57] can be abstracted into this generic
methodology, which can be applied to any other types of
community, both online and offline.
Our studies on how roles influence members’ survival re-
vealed that socially positive roles such as support providers
and newcomer welcomers were associated with staying longer
at CSN. It may be that to take on these socially positive roles,
members have to stay in the group for a while to be familiar
with the group norms and other members; occupying such
roles may also indicate that members already have relation-
ships with and attachment to others or the group as a whole.
The role transition analyses illustrate that members on CSN
enact emergent roles and frequently transit to other roles,
confirming prior work that such roles are transient [2].
Implication
Our research sheds light on how to build more successful
online communities from both practical and theoretical per-
spectives. Theoretically, our work contributes to the un-
derstandings of emergent roles by introducing a general,
four-component role framework. The iterative role identifi-
cation process described here is reproducible broadly within
the HCI community, as are our mixed-methods (quantita-
tive/qualitative) criteria for evaluating the quality of derived
roles. Practically, our role modeling methods can be em-
ployed to develop tools that detect members’ needs, track
their activities, and offer them help and task of interests. Such
identified roles can better help patients know themselves and
others. Future work should focus on incorporating this in-
formation into profile pages and other interface affordances.
ACKNOWLEDGEMENT
This work was supported by NIMH grant R21 MH106880-01
and a grant from Google to Robert Kraut. Diyi Yang was
supported by Facebook Fellowship. The authors would like
to thank Zheng Yao and the reviewers for their feedback.
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